An Aberrant Abundance of Cronbach’s Alpha Values at .70
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cronbach’s α is the most widely reported metric of the reliability of psychological measures. Decisions about an observed α’s adequacy are often made using rule-of-thumb thresholds, such as α of at least .70. Such thresholds can put pressure on researchers to make their measures meet these criteria, similar to the pressure to meet the significance threshold with p values. We examined whether α values reported in the psychology literature are inflated at the rule-of-thumb thresholds (αs = .70, .80, .90) because of, for example, overfitting to in-sample data (α-hacking) or publication bias. We extracted reported α values from three very large data sets covering the general psychology literature (> 30,000 α values taken from > 74,000 published articles in American Psychological Association [APA] journals), the industrial and organizational (I/O) psychology literature (> 89,000 α values taken from > 14,000 published articles in I/O journals), and the APA’s PsycTests database, which aims to cover all psychological measures published since 1894 (> 67,000 α values taken from > 60,000 measures). The distributions of these values show robust evidence of excesses at the α = .70 rule-of-thumb threshold that cannot be explained by justifiable measurement practices. We discuss the scope, causes, and consequences of α-hacking and how increased transparency, preregistration of measurement strategy, and standardized protocols could mitigate this problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it